INTERNAL CHANGE.

PiperOrigin-RevId: 397358689

research/object_detection/builders/model_builder.py

@@ -1182,13 +1182,24 @@ def _build_center_net_feature_extractor(feature_extractor_config, is_training):
           list(feature_extractor_config.channel_stds),
       'bgr_ordering':
           feature_extractor_config.bgr_ordering,
-      'depth_multiplier':
-          feature_extractor_config.depth_multiplier,
-      'use_separable_conv':
-          use_separable_conv,
-      'upsampling_interpolation':
-          feature_extractor_config.upsampling_interpolation,
   }
+  if feature_extractor_config.HasField('depth_multiplier'):
+    kwargs.update({
+        'depth_multiplier': feature_extractor_config.depth_multiplier,
+    })
+  if feature_extractor_config.HasField('use_separable_conv'):
+    kwargs.update({
+        'use_separable_conv': use_separable_conv,
+    })
+  if feature_extractor_config.HasField('upsampling_interpolation'):
+    kwargs.update({
+        'upsampling_interpolation':
+            feature_extractor_config.upsampling_interpolation,
+    })
+  if feature_extractor_config.HasField('use_depthwise'):
+    kwargs.update({
+        'use_depthwise': feature_extractor_config.use_depthwise,
+    })
 
 
   return CENTER_NET_EXTRACTOR_FUNCTION_MAP[feature_extractor_config.type](

research/object_detection/builders/model_builder_tf2_test.py

@@ -476,7 +476,7 @@ class ModelBuilderTF2Test(
         num_classes: 10
         feature_extractor {
           type: "mobilenet_v2_fpn"
-          depth_multiplier: 1.0
+          depth_multiplier: 2.0
           use_separable_conv: true
           upsampling_interpolation: "bilinear"
         }
@@ -513,6 +513,21 @@ class ModelBuilderTF2Test(
     # Verify that there are up_sampling2d layers.
     self.assertGreater(num_up_sampling2d_layers, 0)
 
+    # Verify that the FPN ops uses separable conv.
+    for layer in fpn.layers:
+      # Convolution layers with kernel size not equal to (1, 1) should be
+      # separable 2D convolutions.
+      if 'conv' in layer.name and layer.kernel_size != (1, 1):
+        self.assertIsInstance(layer, tf.keras.layers.SeparableConv2D)
+
+    # Verify that the backbone indeed double the number of channel according to
+    # the depthmultiplier.
+    backbone = feature_extractor.get_layer('model')
+    first_conv = backbone.get_layer('Conv1')
+    # Note that the first layer typically has 32 filters, but this model has
+    # a depth multiplier of 2.
+    self.assertEqual(64, first_conv.filters)
+
   def test_create_center_net_deepmac(self):
     """Test building a CenterNet DeepMAC model."""